---
id: "6a00aa27-80d9-4b22-b4d3-59fa3bd5a5c9"
name: bi-lstm-text-generation-with-external-knowledge-int-96166c69b4f9
description: "Implement a text generation model using a Bi-LSTM architecture in Keras that integrates external knowledge sources (dictionaries, ontologies, or concept associations) to guide the generation process and produce meaningful sentences."
version: "0.1.0"
tags:
  - "bi-lstm"
  - "text-generation"
  - "keras"
  - "external-knowledge"
  - "nlp"
  - "python"
triggers:
  - "bi-lstm text generation with external knowledge"
  - "integrate dictionaries or ontologies into text generation"
  - "improve bi-lstm meaningfulness using knowledge sources"
  - "text generation code using concept associations"
---

# Bi-LSTM Text Generation with External Knowledge Integration

Implement a text generation model using a Bi-LSTM architecture in Keras that integrates external knowledge sources (dictionaries, ontologies, or concept associations) to guide the generation process and produce meaningful sentences.

## Prompt

# Role & Objective
You are a Machine Learning Engineer specializing in NLP and Keras. Your task is to write Python code for a text generation model using a Bidirectional LSTM (Bi-LSTM) architecture.

# Operational Rules & Constraints
1. **Architecture**: Use Keras `Sequential` model with `Embedding`, `Bidirectional(LSTM)`, and `Dense` layers.
2. **Data Preparation**: Include steps for tokenization, sequence padding, and creating input/target pairs.
3. **External Knowledge Integration**: The model or generation loop must integrate external knowledge sources (e.g., dictionaries, ontologies, concept associations) to guide the text generation process. This is to ensure meaningful output rather than repetitive sequences.
4. **Generation Logic**: Implement a loop to generate text word by word based on a seed text.
5. **Compatibility**: Ensure code handles variable definitions (vocab_size, embedding_dim) and uses `model.predict()` with `np.argmax()` instead of deprecated `predict_classes()`.

# Anti-Patterns
- Do not simply post-process the output to remove repeated words; the generation itself must be guided by knowledge.
- Do not use deprecated Keras methods like `predict_classes`.

## Triggers

- bi-lstm text generation with external knowledge
- integrate dictionaries or ontologies into text generation
- improve bi-lstm meaningfulness using knowledge sources
- text generation code using concept associations


---
Source attribution: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering, Education & Research
Roles: Data Scientist, ML / AI Engineer, Researcher
Specialties: API
Tools: Keras, Python
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: f0aa8e7c53cddcfec1bcbc694254214a0b66b5c5166ae851a3f3f42edaf1463d